US11850399B2ActiveUtilityA1
Feedback predictive control approach for processes with time delay in the manipulated variable
Assignee: UNIV IOWA STATE RES FOUND INCPriority: Jul 13, 2018Filed: Sep 9, 2021Granted: Dec 26, 2023
Est. expiryJul 13, 2038(~12 yrs left)· nominal 20-yr term from priority
A61M 5/1723A61M 5/14248G16H 20/17A61M 2205/3327A61M 2205/3334A61M 2205/3368A61M 2205/50A61M 2205/505A61M 2205/52A61M 2230/201A61M 2230/50A61M 2230/62A61M 2230/63A61M 5/14244A61M 5/16804
65
PatentIndex Score
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Cited by
43
References
20
Claims
Abstract
This invention relates to a feedback predictive controller, systems comprising and methods employing the same. Preferably the feedback predictive controller and/or systems comprising the feedback predictive controller are part of an automatic insulin delivery system. The methods described herein can be used to control blood glucose concentration in a patient with diabetes. Preferably, the insulin delivery system is an artificial pancreas.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1. A method of using an insulin delivery system comprising steps of:
determining an amount of insulin to administer using a feedback predictive control, wherein the feedback predictive control is stored on a machine readable non-transitory media;
wherein the feedback predictive control comprises a first model and a second model;
wherein the first model is a predictive model; and wherein the second model is a noise model, which models unmeasured disturbances and bias;
providing a predictive input and a measured input to the first model;
using the predictive input and measured input to provide a parametrized first model to provide an output, wherein the output is provided to the second model and to a PID controller, wherein the PID controller controls an insulin flow rate;
providing feedback to the PID controller from the second model;
administering insulin using the insulin delivery system based on the output and the feedback, wherein the administration of insulin is adjusted to nullify the effect of any disturbances on blood glucose concentration based on a predicted blood glucose value.
2. The method of claim 1 wherein the measured input is provided by an automatic monitoring system.
3. The method of claim 2 wherein the automatic monitoring system comprises one or more sensors comprising at least one of the following a soft sensor, a remote sensor, an accelerometer, or a thermistor.
4. The method of claim 2 wherein the automatic monitoring system monitors at least one of the following variables body position, movement, heat dissipated, skin temperature, near body temperature, galvanic skin response, and sleep, basal insulin, or bolus insulin.
5. The method of claim 1 wherein the predictive input is provided manually by a user.
6. The method of claim 5 wherein the predictive input provided manually by the user includes at least one of the following consumed energy, basal insulin, or bolus insulin.
7. The method of claim 1 wherein the insulin delivery system is a wearable device, an artificial pancreas, or a combination thereof.
8. The method of claim 1 wherein the insulin delivery system comprises an apparatus for administering insulin.
9. The method of claim 8 wherein the apparatus for administering insulin is an automatic insulin pump, a remotely controlled insulin pump, an IV, a catheter, or an artificial pancreas.
10. The method of claim 1 , wherein the first model is:
f ( V )= n t +ϵ t =a 0 +V FI,t +a A1 v A l,t +. . . +a Ap V A p, t
and wherein the second model is
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11. A system for administering insulin, the system comprising:
an apparatus for administering insulin;
a sensor;
a PID controller comprising a feedback predictive control stored on a machine readable non-transitory media associated with a computing device, the computing device in operative communication with the sensor; wherein the feedback predictive control comprises a first model and a second model; wherein the first model is a predictive model; and wherein the second model is a noise model, which models unmeasured disturbances and bias;
wherein the machine readable non-transitory media is capable of receiving a predictive input and a measured input and wherein said predictive input and/or measured input are parametrized by the first model to provide an output, wherein the output is provided to the second model and to a PID controller, wherein the PID controller controls an insulin flow rate, wherein the second model provides feedback to the PID controller, wherein the PID controller controls an insulin flow rate.
12. The system of claim 11 further comprising a monitoring system, wherein at least one of said measured input is provided by the monitoring system.
13. The system of claim 12 wherein the monitoring system monitors at least one of the following variables body position, movement, heat dissipated, skin temperature, near body temperature, galvanic skin response, and sleep, basal insulin, or bolus insulin.
14. The system of claim 13 wherein the one or more inputs provided manually by the user includes at least one of the following consumed energy, basal insulin, or bolus insulin.
15. The system of claim 11 wherein at least one of said predictive input is provided manually by a user.
16. The system of claim 11 wherein the plurality of sensors comprise at least one of the following a soft sensor, a remote sensor, an accelerometer, or a thermistor.
17. The system of claim 11 wherein the insulin delivery system is a wearable device, an artificial pancreas, or a combination thereof.
18. The system of claim 11 wherein the first model is:
f ( V )= n t +ϵ t =a 0 +V FI,t +a A1 v Al,t +. . . +a Ap V Ap,t
and wherein the second model is
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wherein e t is a residual at t of the first model, and subject to:
subject to Σ i=1 4 φ 1 =1.
19. The system of claim 11 , wherein the apparatus for administering insulin is an automatic insulin pump, a remotely controlled insulin pump, an IV, a catheter, or an artificial pancreas.
20. The system of claim 11 , wherein the output is recorded, printed, and/or displayed.Join the waitlist — get patent alerts
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